Multi-objective optimization of Stirling engine using non-ideal adiabatic method

In the recent years, remarkable attention is drawn to Stirling engine due to noticeable advantages, for instance a lot of resources such as biomass, fossil fuels and solar energy can be applied as heat source. Great numbers of studies are conducted on Stirling engines and non-ideal adiabatic method is one of them. In the present study, the efficiency and the power loss due to pressure drop into the heat exchangers are optimized for a Stirling system using non-ideal adiabatic analysis and the second-version Non-dominated Sorting Genetic Algorithm. The optimized answers are chosen from the results using three decision-making methods. The applied methods were compared at last and the best results were obtained for the technique for order preference by similarity to ideal solution decision making method.